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"\n\n# Discriminability Testing\n\nIf you have repeated measures from the same subject, and want to see if these are\ndifferent than those from other subjects. Let's look at the mathematical formulations:\n\nWith $D_x$ as the sample discriminability of $x$, one sample test performs\nthe following test:\n\n\\begin{align}H_0 &: D_x = D_0 \\\\\n H_A &: D_x > D_0\\end{align}\n\nwhere $D_0$ is the discriminability that would be observed by random chance.\n\nThis can also be formulated as a two-sample test. Let $\\hat{D}_x$ denote the\nsample discriminability of one approach, and $\\hat{D}_y$ denote the sample\ndiscriminability of another approach. Then,\n\n\\begin{align}H_0 &: D_x = D_y \\\\\n H_A &: D_x > D_y\\end{align}\n\nAlternative tests can be done for $D_x < D_y$ and $D_x \\neq D_y$.\n\nLike all the other tests within hyppo, each method has a :func:`statistic` and\n:func:`test` method. The :func:`test` method is the one that returns the test statistic\nand p-values, among other outputs, and is the one that is used most often in the\nexamples, tutorials, etc.\nThe p-value returned is calculated using a permutation test.\n"
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"**Discrimnability one-sample** and\n**Discrimnability two-sample** are time series tests of independence.\nMore details can be found in :class:`hyppo.discrim.DiscrimOneSample` and\n:class:`hyppo.discrim.DiscrimTwoSample`.\n\nEach class has a ``is_dist`` parameter that indicates whether or not inputs are\ndistance matrices. These distances must be euclidean distance.\nAlso, ``remove_isolates`` indicates whether or not to remove measurements with a single\ninstance.\nOtherwise, these tests runs like `any other test`.\n\n"
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